Modelling ocean acidification effects with life stage-specific responses alters spatiotemporal patterns of catch and revenues of American lobster, Homarus americanus
Bibliographic record
Abstract
Ocean acidification (OA) affects marine organisms through various physiological and biological processes, yet our understanding of how these translate to large-scale population effects remains limited. Here, we integrated laboratory-based experimental results on the life history and physiological responses to OA of the American lobster, Homarus americanus, into a dynamic bioclimatic envelope model to project future climate change effects on species distribution, abundance, and fisheries catch potential. Ocean acidification effects on juvenile stages had the largest stage-specific impacts on the population, while cumulative effects across life stages significantly exerted the greatest impacts, albeit quite minimal. Reducing fishing pressure leads to overall increases in population abundance while setting minimum size limits also results in more higher-priced market-sized lobsters (> 1 lb), and could help mitigate the negative impacts of OA and concurrent stressors (warming, deoxygenation). However, the magnitude of increased effects of climate change overweighs any moderate population gains made by changes in fishing pressure and size limits, reinforcing that reducing greenhouse gas emissions is most pressing and that climate-adaptive fisheries management is necessary as a secondary role to ensure population resiliency. We suggest possible strategies to mitigate impacts by preserving important population demographics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".